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Mathematical Problems in Engineering
Volume 2015, Article ID 504725, 14 pages
http://dx.doi.org/10.1155/2015/504725
Research Article

A Novel Multiscale Edge Detection Approach Based on Nonsubsampled Contourlet Transform and Edge Tracking

School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China

Received 28 June 2014; Accepted 23 October 2014

Academic Editor: Jun Jiang

Copyright © 2015 Enqing Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Yi, D. Labate, G. R. Easley, and H. Krim, “A shearlet approach to edge analysis and detection,” IEEE Transactions on Image Processing, vol. 18, no. 5, pp. 929–941, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. I. Sobel, Camera Models and Machine Perception, Department of Computer Science, Stanford University, 1970.
  3. J. M. S. Prewitt, “Object enhancement and extraction,” Picture Processing and Psychopictorics, vol. 10, no. 1, pp. 15–19, 1970. View at Google Scholar
  4. A. Rosenfeld, “The max Roberts operator is a Hueckel-type edge detector,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, no. 1, pp. 101–103, 1981. View at Google Scholar · View at Scopus
  5. J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986. View at Google Scholar · View at Scopus
  6. C. Lopez-Molina, B. de Baets, and H. Bustince, “Quantitative error measures for edge detection,” Pattern Recognition, vol. 46, no. 4, pp. 1125–1139, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 7, pp. 710–732, 1992. View at Google Scholar
  8. S. Mallat and W. L. Hwang, “Singularity detection and processing with wavelets,” IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 617–643, 1992. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. M.-Y. Shih and D.-C. Tseng, “A wavelet-based multiresolution edge detection and tracking,” Image and Vision Computing, vol. 23, no. 4, pp. 441–451, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Heric and D. Zazula, “Combined edge detection using wavelet transform and signal registration,” Image and Vision Computing, vol. 25, no. 5, pp. 652–662, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. E. Brannock and M. Weeks, “A synopsis of recentwork in edge detection using the DWT,” in Proceedings of the IEEE Southeastcon, pp. 515–520, Huntsville, Ala, USA, April 2008. View at Publisher · View at Google Scholar
  12. W. Jiang, K.-M. Lam, and T.-Z. Shen, “Efficient edge detection using simplified Gabor wavelets,” IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 39, no. 4, pp. 1036–1047, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2091–2106, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Zhou, A. L. Cunha, and M. N. Do, “Nonsubsampled contourlet transform: construction and application in enhancement,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '05), vol. 1, pp. 469–472, Genova, Italy, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. A. L. da Cunha, J. Zhou, and M. N. Do, “The nonsubsampled contourlet transform: theory, design, and applications,” IEEE Transactions on Image Processing, vol. 15, no. 10, pp. 3089–3101, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898–916, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. A. L. da Cunha, J. Zhou, and M. N. Do, “Nonsubsampled contourlet transform: filter design and applications in denoising,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '05), vol. 1, pp. 749–752, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. K. G. Derpanis, “K-Means Clustering,” 2006.
  19. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient k-means clustering algorithms: analysis and implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881–892, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. P. Wang, H. Tian, and W. Zheng, “A novel image fusion method based on FRFT-NSCT,” Mathematical Problems in Engineering, vol. 2013, Article ID 408232, 9 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  21. Y. Tong, M. Zhao, Z. Wei, and L. Liu, “Synthetic Aperture Radar image nonlinear enhancement algorithm based on NSCT transform,” Physical Communication, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Ergen, “A fusion method of gabor wavelet transform and unsupervised clustering algorithms for tissue edge detection,” The Scientific World Journal, vol. 2014, Article ID 964870, 13 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. C. Lopez-Molina, B. de Baets, H. Bustince, J. Sanz, and E. Barrenechea, “Multiscale edge detection based on Gaussian smoothing and edge tracking,” Knowledge-Based Systems, vol. 44, pp. 101–111, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. D. Marr and E. Hildreth, “Theory of edge detection,” Proceedings of the Royal Society of London Series B: Biological Sciences, vol. 207, no. 1167, pp. 187–217, 1980. View at Publisher · View at Google Scholar · View at Scopus
  25. D. R. Martin, C. C. Fowlkes, and J. Malik, “Learning to detect natural image boundaries using local brightness, color, and texture cues,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 530–549, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. P. Bao, L. Zhang, and X. Wu, “Canny edge detection enhancement by scale multiplication,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 9, pp. 1485–1490, 2005. View at Publisher · View at Google Scholar · View at Scopus